A Hexapod Robot Gait Planning Method Based on Deep Reinforcement Learning

A hexapod robot and reinforcement learning technology, which is applied in the field of gait planning of hexapod robots based on deep reinforcement learning, can solve problems such as the inability to realize gait planning, achieve the effects of reducing impact, improving the motion system, and improving performance

Active Publication Date: 2021-08-10
唐开强
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AI Technical Summary

Problems solved by technology

[0003] The technical problem to be solved by the present invention is that the traditional pre-programmed method of the hexapod robot cannot realize the unstructured environment of gait planning

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  • A Hexapod Robot Gait Planning Method Based on Deep Reinforcement Learning
  • A Hexapod Robot Gait Planning Method Based on Deep Reinforcement Learning
  • A Hexapod Robot Gait Planning Method Based on Deep Reinforcement Learning

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Embodiment Construction

[0056] Such as figure 1 As shown, the gait planning method for a hexapod robot based on deep reinforcement learning disclosed in the present invention includes the steps of acquiring environmental information, completing environmental modeling, screening suitable footholds, formulating motion strategies, executing motion strategies, and starting to walk. Specifically, :

[0057] Step 1, obtain environmental information, and the hexapod robot obtains environmental road condition information through satellite maps and cameras;

[0058] Step 2: Carry out environmental modeling, discretize the obtained environmental road condition information by means of subdividing and discretizing the equal area, and segment the image as figure 2 As shown, the environmental road condition information is transformed into scattered and independent footholds similar to plum blossom piles, as shown in image 3 shown;

[0059] Step 3, screening of footholds. According to the hexapod robot’s own p...

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Abstract

The invention provides a gait planning method for a hexapod robot based on deep reinforcement learning, the steps include: obtaining environmental information, performing environment modeling, screening footholds, formulating upper-layer motion strategies, lower-layer execution strategies, and robot motion drive six steps . The gait planning method of the hexapod robot enables the hexapod robot to use deep learning and reinforcement learning algorithms to solve the optimal path in the plum blossom maze problem derived from the abstraction of the environment, and to select a suitable foothold according to the optimal path , so as to achieve efficient walking in unstructured environments.

Description

technical field [0001] The present invention relates to a robot gait planning method, in particular to a hexapod robot gait planning method based on deep reinforcement learning. Background technique [0002] The hexapod robot has multiple redundant degrees of freedom in structure, so it has a high adaptability to the terrain environment. Hexapod robots can walk in the field with complex road conditions, overcome obstacles, and complete transportation operations in non-structural environments that cannot be completed by wheeled or tracked types. They are used in forest logging, mining, underwater construction, nuclear industry, military transportation and It has a very broad application prospect in fields such as detection and planetary detection. Therefore, the research on hexapod robots has always attracted the attention of experts and scholars from various countries, but how to improve the mobility of hexapod robots in unstructured environments is still an unresolved issu...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G05D1/02
Inventor 唐开强留沧海孙建洪俊刘佳生侯跃南艾攀华潘东旭钱勇
Owner 唐开强
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